{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "### 1、加载log.txt文件(未对文件进列索引设置，直接将索引标题写进log文件，并直接加载出来)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>api</th>\n",
       "      <th>count</th>\n",
       "      <th>res_time_sum</th>\n",
       "      <th>res_time_min</th>\n",
       "      <th>res_time_max</th>\n",
       "      <th>res_time_avg</th>\n",
       "      <th>interval</th>\n",
       "      <th>created_at</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>162542</td>\n",
       "      <td>/front-api/bill/create</td>\n",
       "      <td>8</td>\n",
       "      <td>1057.31</td>\n",
       "      <td>88.75</td>\n",
       "      <td>177.72</td>\n",
       "      <td>132.0</td>\n",
       "      <td>60</td>\n",
       "      <td>2017-11-01 00:00:07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>162644</td>\n",
       "      <td>/front-api/bill/create</td>\n",
       "      <td>5</td>\n",
       "      <td>749.12</td>\n",
       "      <td>103.79</td>\n",
       "      <td>240.38</td>\n",
       "      <td>149.0</td>\n",
       "      <td>60</td>\n",
       "      <td>2017-11-01 00:01:07</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       id                     api  count  res_time_sum  res_time_min  \\\n",
       "0  162542  /front-api/bill/create      8       1057.31         88.75   \n",
       "1  162644  /front-api/bill/create      5        749.12        103.79   \n",
       "\n",
       "   res_time_max  res_time_avg  interval           created_at  \n",
       "0        177.72         132.0        60  2017-11-01 00:00:07  \n",
       "1        240.38         149.0        60  2017-11-01 00:01:07  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "log = pd.read_csv(r'C:\\Users\\王湛淇\\Desktop\\log.txt',sep='\\t')\n",
    "#log.loc[870]\n",
    "log.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "#也尝试过设置行索引，但加载出来的值均为空值\n",
    "#lg = pd.DataFrame(log,columns=['id','api','count','res_time_sum','res_time_min','res_time_max','res_time_avg','interval','created_at'])\n",
    "#lg.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "### 2、检测是否有重复值，结果显示为0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "log.duplicated().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>count</th>\n",
       "      <th>res_time_sum</th>\n",
       "      <th>res_time_min</th>\n",
       "      <th>res_time_max</th>\n",
       "      <th>res_time_avg</th>\n",
       "      <th>interval</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>1.794960e+05</td>\n",
       "      <td>179496.000000</td>\n",
       "      <td>179496.000000</td>\n",
       "      <td>179496.000000</td>\n",
       "      <td>179496.000000</td>\n",
       "      <td>179496.000000</td>\n",
       "      <td>179496.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>6.866490e+06</td>\n",
       "      <td>7.175909</td>\n",
       "      <td>1393.177370</td>\n",
       "      <td>108.419620</td>\n",
       "      <td>359.880351</td>\n",
       "      <td>187.812208</td>\n",
       "      <td>60.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>3.686579e+06</td>\n",
       "      <td>4.325160</td>\n",
       "      <td>1499.485881</td>\n",
       "      <td>79.640559</td>\n",
       "      <td>638.919769</td>\n",
       "      <td>224.464813</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.625420e+05</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>36.550000</td>\n",
       "      <td>3.210000</td>\n",
       "      <td>36.550000</td>\n",
       "      <td>36.000000</td>\n",
       "      <td>60.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>3.825183e+06</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>607.707500</td>\n",
       "      <td>83.410000</td>\n",
       "      <td>198.280000</td>\n",
       "      <td>144.000000</td>\n",
       "      <td>60.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>6.811432e+06</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>1154.905000</td>\n",
       "      <td>97.120000</td>\n",
       "      <td>256.090000</td>\n",
       "      <td>167.000000</td>\n",
       "      <td>60.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>9.981397e+06</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>1834.117500</td>\n",
       "      <td>116.990000</td>\n",
       "      <td>374.410000</td>\n",
       "      <td>202.000000</td>\n",
       "      <td>60.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1.343909e+07</td>\n",
       "      <td>31.000000</td>\n",
       "      <td>142650.550000</td>\n",
       "      <td>18896.640000</td>\n",
       "      <td>142468.270000</td>\n",
       "      <td>71325.000000</td>\n",
       "      <td>60.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 id          count   res_time_sum   res_time_min  \\\n",
       "count  1.794960e+05  179496.000000  179496.000000  179496.000000   \n",
       "mean   6.866490e+06       7.175909    1393.177370     108.419620   \n",
       "std    3.686579e+06       4.325160    1499.485881      79.640559   \n",
       "min    1.625420e+05       1.000000      36.550000       3.210000   \n",
       "25%    3.825183e+06       4.000000     607.707500      83.410000   \n",
       "50%    6.811432e+06       7.000000    1154.905000      97.120000   \n",
       "75%    9.981397e+06      10.000000    1834.117500     116.990000   \n",
       "max    1.343909e+07      31.000000  142650.550000   18896.640000   \n",
       "\n",
       "        res_time_max   res_time_avg  interval  \n",
       "count  179496.000000  179496.000000  179496.0  \n",
       "mean      359.880351     187.812208      60.0  \n",
       "std       638.919769     224.464813       0.0  \n",
       "min        36.550000      36.000000      60.0  \n",
       "25%       198.280000     144.000000      60.0  \n",
       "50%       256.090000     167.000000      60.0  \n",
       "75%       374.410000     202.000000      60.0  \n",
       "max    142468.270000   71325.000000      60.0  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "log.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "###3、假设最大响应时间超过8848为异常值，显示结果为51条"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "51"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sum(log.res_time_max>8848)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "###4、api不显示在log.describe中，而interval均为同一个值，因此该两列数据对分析无用，可丢弃"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "#排序报错——unexpected EOF while parsing\n",
    "#ser=pd.Series(log,index=list(log['created_at'])\n",
    "#ser.sort_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "###6、分析api调用次数情况,先取出一天的数据，再取出count和created_at"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>api</th>\n",
       "      <th>count</th>\n",
       "      <th>res_time_sum</th>\n",
       "      <th>res_time_min</th>\n",
       "      <th>res_time_max</th>\n",
       "      <th>res_time_avg</th>\n",
       "      <th>interval</th>\n",
       "      <th>created_at</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>868</th>\n",
       "      <td>244507</td>\n",
       "      <td>/front-api/bill/create</td>\n",
       "      <td>7</td>\n",
       "      <td>1191.49</td>\n",
       "      <td>104.12</td>\n",
       "      <td>215.48</td>\n",
       "      <td>170.0</td>\n",
       "      <td>60</td>\n",
       "      <td>2017-11-01 23:57:09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>869</th>\n",
       "      <td>244632</td>\n",
       "      <td>/front-api/bill/create</td>\n",
       "      <td>3</td>\n",
       "      <td>606.30</td>\n",
       "      <td>148.78</td>\n",
       "      <td>250.93</td>\n",
       "      <td>202.0</td>\n",
       "      <td>60</td>\n",
       "      <td>2017-11-01 23:58:09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>870</th>\n",
       "      <td>244692</td>\n",
       "      <td>/front-api/bill/create</td>\n",
       "      <td>2</td>\n",
       "      <td>225.11</td>\n",
       "      <td>100.76</td>\n",
       "      <td>124.35</td>\n",
       "      <td>112.0</td>\n",
       "      <td>60</td>\n",
       "      <td>2017-11-01 23:59:09</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         id                     api  count  res_time_sum  res_time_min  \\\n",
       "868  244507  /front-api/bill/create      7       1191.49        104.12   \n",
       "869  244632  /front-api/bill/create      3        606.30        148.78   \n",
       "870  244692  /front-api/bill/create      2        225.11        100.76   \n",
       "\n",
       "     res_time_max  res_time_avg  interval           created_at  \n",
       "868        215.48         170.0        60  2017-11-01 23:57:09  \n",
       "869        250.93         202.0        60  2017-11-01 23:58:09  \n",
       "870        124.35         112.0        60  2017-11-01 23:59:09  "
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "l=log.loc[log.index[:871]].copy()\n",
    "l.tail(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>created_at</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2017-11-01 00:00:07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2017-11-01 00:01:07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2017-11-01 00:02:07</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            created_at\n",
       "0  2017-11-01 00:00:07\n",
       "1  2017-11-01 00:01:07\n",
       "2  2017-11-01 00:02:07"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a,crat=np.split(l,[8],axis=1)\n",
    "crat.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
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       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  count\n",
       "0     8\n",
       "1     5"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "b,c=np.split(l,[3],axis=1)\n",
    "d,cont=np.split(b,[2],axis=1)\n",
    "cont.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\users\\王湛淇\\appdata\\local\\programs\\python\\python37\\lib\\site-packages\\ipykernel_launcher.py:2: FutureWarning: Method .as_matrix will be removed in a future version. Use .values instead.\n",
      "  \n",
      "c:\\users\\王湛淇\\appdata\\local\\programs\\python\\python37\\lib\\site-packages\\ipykernel_launcher.py:3: FutureWarning: Method .as_matrix will be removed in a future version. Use .values instead.\n",
      "  This is separate from the ipykernel package so we can avoid doing imports until\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "871"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#将pandas的ser格式的数据转换成ndarray格式的,并转换成一维数组\n",
    "cr=crat.as_matrix()\n",
    "ct=cont.as_matrix()\n",
    "len(cr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "#将二维数组转化成一维数组\n",
    "x1=[]\n",
    "for i in range(len(cr)):\n",
    "    x1.append(cr[i][0])\n",
    "y1=[]\n",
    "for i in range(len(ct)):\n",
    "    y1.append(ct[i][0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(x1,y1)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "###7、分析api响应时间"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>res_time_min</th>\n",
       "      <th>res_time_max</th>\n",
       "      <th>res_time_avg</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>88.75</td>\n",
       "      <td>177.72</td>\n",
       "      <td>132</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>103.79</td>\n",
       "      <td>240.38</td>\n",
       "      <td>149</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  res_time_min res_time_max res_time_avg\n",
       "0        88.75       177.72          132\n",
       "1       103.79       240.38          149"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "e,f=np.split(l,[4],axis=1)\n",
    "g,h=np.split(f,[3],axis=1)\n",
    "g.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>res_time_avg</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>132</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>149</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  res_time_avg\n",
       "0          132\n",
       "1          149"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "min,i=np.split(g,[1],axis=1)\n",
    "max,avg=np.split(i,[1],axis=1)\n",
    "avg.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(x1,min)\n",
    "plt.plot(x1,max)\n",
    "plt.plot(x1,avg)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "###8、分析连续的⼏天数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>api</th>\n",
       "      <th>count</th>\n",
       "      <th>res_time_sum</th>\n",
       "      <th>res_time_min</th>\n",
       "      <th>res_time_max</th>\n",
       "      <th>res_time_avg</th>\n",
       "      <th>interval</th>\n",
       "      <th>created_at</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>5214</th>\n",
       "      <td>633814</td>\n",
       "      <td>/front-api/bill/create</td>\n",
       "      <td>5</td>\n",
       "      <td>808.39</td>\n",
       "      <td>93.23</td>\n",
       "      <td>329.14</td>\n",
       "      <td>161.0</td>\n",
       "      <td>60</td>\n",
       "      <td>2017-11-07 00:00:20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5215</th>\n",
       "      <td>633838</td>\n",
       "      <td>/front-api/bill/create</td>\n",
       "      <td>5</td>\n",
       "      <td>844.69</td>\n",
       "      <td>148.38</td>\n",
       "      <td>206.42</td>\n",
       "      <td>168.0</td>\n",
       "      <td>60</td>\n",
       "      <td>2017-11-07 00:01:20</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          id                     api  count  res_time_sum  res_time_min  \\\n",
       "5214  633814  /front-api/bill/create      5        808.39         93.23   \n",
       "5215  633838  /front-api/bill/create      5        844.69        148.38   \n",
       "\n",
       "      res_time_max  res_time_avg  interval           created_at  \n",
       "5214        329.14         161.0        60  2017-11-07 00:00:20  \n",
       "5215        206.42         168.0        60  2017-11-07 00:01:20  "
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "t=log.loc[log.index[:5216]].copy()\n",
    "t.tail(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<function matplotlib.pyplot.show(*args, **kw)>"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "t['count'].plot()\n",
    "plt.show"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "###9、分析周末访问量是否有增加，提取一个周末数据和非周末进行访问量对比"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "t1=log.loc[log.index[2612:3488]].copy()\n",
    "t2=log.loc[log.index[:872]].copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "t3=t1['count'].copy()\n",
    "t4=t1['count'].copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "x2=np.arange(876)+1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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5IoO7psZf3R5/yQanmTnr2yX1RJJ8lLGPizL8ipJChuFnWoTBOFcseKhN7WGXSYRzGn3QldTgSyGOnx8KTBE8fkcIbdUPrpcHZfgV8iSQ18XykCNFYURPEmePvkl25m51G55iRCXptmRO4tq+Lgko1FhJYZ/VH1VVHpThV4SnXBq/aSsiHN5hIBIJ5zSo9sHdIqBzxlCYMJwzvNTjLOKiDH8UlOFXRCCO4TcMZRSpJ8bxHQOCWnJx/HrVp2VOvv86z6xYYyHG4K5nAleYftiMvfL4IyFTevFhItpFRCtsy+YS0TYiWmr8XS7Y9lIiWktEG4jo1iQ7rqhSWBzDH91IOSWBJKJ6jOycKmWDB57HHzw+Ey1Xj/L4oyHj+jwK4FLO8nsYY9ONv+fdK4koBeBnAC4DcBqAq4notDidVVQ/ppZLccI5I0g1To0/wZQNSurxYPf4CzV33a2cEg+F8fiZGtyNS+AVwBibD2BfhH3PBLCBMbaRMdYN4FcAroywH0WZWP/WfGxcsci2JHpUjYWl8ft7/Cte/11+BqiNMJN8mK5j0W9+jHVL/pp/b5/mf3AH3n71Kd9t3cf2tCEznDO/363rl2HNonCTwKKw8vU/YNvG+KUjLUIazh1bN2D5/GcBAB3792DJi//jacOVeoynNc/Aunn8iIO7QR7/khf/Bx37dlvv/c7f8vnPYseW9Viz6CVsXb8sRH+qjzizaL5ERJ8B0ArgZsbYftf60QC22t63AZgl2hkRXQfgOgAYN25cjG4pkmLSsx/Mv5iSr2sanGExGJKM6pny8qcAAN0XX4O6+gbnPiSOs7PtHcxa+R3sWzkAaN7q8Phnb7wX2Cjedvn8Z6zZpyJMqUfP5T/P2MfPy6+Y1SHRu+hMfvkT+Rdzi3scEf0fPBsjqBM4rwPv3vcxNHe2YvspMzHy+JOtNlxTLPjtWOmZow7u+ty4dmxZj+YFX8Lbb7Xg9FtfAeB//qb++TM4zPpgBB0TtqkVoj7z/hzAiQCmA9gO4MecNrzrU/gtMcbuZ4y1MMZampqaInZLUfEYHjJJavzcOHmJJ45cthsAMBgHjePKG5buQ3sC21hCRZVLPWEHdxup03p9XFc7ACDb3SlqXjiOMDtnhDh+yXDO7s4jjn7K0M80+jVOJMPPGNvJGMux/K/mAeRlHTdtAMba3o8BIP8NKGoTa3BX7mHTcWGbxkHC8LtTKYQaxJRq65R6eiMiLz1lG4T3VOASpGwIp/HLST3JzNeoTSIZfiIaaXv7jwBWcJq9CWASEU0gojoAHwfwXJTjKSqF+ANpZGj80h4/16OWMPyuVAphDLRMW1YzhVhibZ3/5xost98QRIbfPYErXFSPnNQT5mZf7dFZYQl0u4joCQAXABhKRG0A7gBwARFNR/7b2gzgeqPtKAAPMsYuZ4xliehLAF4EkALwMGNsZVE+haI0xEiZYGJ5dik5j1+POFnHPaM2nMcvY/iN4/Rij1+ExvueAs5/KI8/xOCuLLlcNtaAZ7UR+FkZY1dzFj8kaNsO4HLb++cBeEI9FdVODMNvevySKRt0nicmc+PR3R5/mNJ+wW2tqJ5q9xQTCIck1+9Bc0g91oGMBc4kbZFy9RQhnDOb7elVhl/N3FWUFDOqhyQ9fseFHcZ4I7rHL+d91kZ2zjiINH6NM9HOStkAl8ZvLS+vxp/L9sgfvwZQhl9RUgrhnHIXpTNqRj6c1B1tE87jl5F6DMOfq26PP87MXfNbIM0doml/LdL4rR4410vAIjoDfmR7lOFXKLiQ+3E9ClZuFrkLlju4K3H8OBq/jHxjGn70Yo+/gNOMaMTzyN3n3x3VE+I8FqEQixn+21tQhl8hT5ITuCQNMd+7kzi+22sP5RlKtK0VjT+ExNLT3eVa4s646cUM7WSM4cihA7BMjulDuLV+KZL3+PVs76qroAy/oigseOhmYO5AYO5AHD5YmNStWZ6drOHXseSPjwJzB2L27t/kF3IMzfK/Pg3MHYhNKxdZ24Vm7kBsWvWmZFSPfxz/0TuGYe13hRPVE6Or82i+sPkjt0hvEzXlw5YfzHa8F6XQOIhG63XK8P5nLvs2Gn98PGa8cZOxxunxTz+6QL4jIQd3/W4qbzzzX8Dcgdi3Y5P88WsAZfgVRWHalkIOl4499nl7wRdqzuZ9MV0HVvw2cJujy38PANj59p8K29mRvBHsWvnXRAx/X+rCydk1UseMw9FD+bQCp7z7uPQ2O9cutF6HqRl8Ys6Z58K6/bpuxBv7nh64L4/2HwbbNr5bSyTj67f6SQDAgXffDt+PKkYZfkUI5C9Se2SH3biYXqKf5p616a26noNOGVcLTqGPVJ2xcZexf6dBTn4yD5kdlN5vMTDHQFgY+Y3sQZYJSCWucysVFRVJ4uEcL+Y8Ciu9dk9w2olaQhl+RWhkwuS4k3js+Bhie2gdYwxMcxp+3vFZOp/IjZmGX49u+JPw+IsB74ZkHj+c4Y9TBMdxdKMP7jw8Muckwoxdz7bx0Y1zYRp+PUp5typEGX5FeCQMf2CtVZ+Lt8cWWsf0HJhMXp90ff6/0OOPOEFI1MQa3C3doCB3MpvxNBXG8DtunBFvXEzXbd+xcx+hvPhIUk/YqB6ftA6m4Td+N9wiMjWIMvyKosCbxJMnWOrR7R6/zgoyjgnnxkOG4aes8cheZI/fmsBVwmLrvNBWM2UEPwd+8bCfT/c5CJN+IYrUYx9UJp/N3TOKeTBzHMAy/L3DJPaOT6koOSm/KxKAnxeWy7mlHrfHz7mgTcOf6zb2HiNXj4QxKkccP+8zRNH47Qnyok7gYowVqmu5zpfMLFx3ds5Qx7anZfY5lsz4hSn1IJc3/LleYhJ7x6dUJIPlycWJ42eO/zwcGr+eA1waP9/jz2v8lONr/KEMjMwELiq9xs/z+Fkkw59AzWFHSKVL6gklq0U5uGQ4p5Rklzf8ZEqESupRKATEynMuEc5p1/hZDszU733QMobh1w2P3228Q0gycsa89FE9/MFdV3+kKFz2Uec/OaQed1SPjDWPko7Zs21QM5mJePlzYToMSupRKBLC/shNEhq/XerRdeb1+DlQJj8OkDI9fnc+/qgzQ8VHNI5Tbo8/P7ish7kZJ1CgxN4X9w1JxuOPUnKxcEC5mbtSqTdMj9+QCHWJ2P9aoHd8SkXRYLqOBY/c4l+cmmsI5AZ3wXRv7n7bxb5r2yYsuP9Ga1nz4b962uTfJhzOaaUXLqXh9x7LLE0pI1EcPdyBhT+/Hnr30cLCqFE9No2//e+/spYv+vWPcGpPcNmNaccWYdFv7sYZR16LcPRCn+dsuc+vk463C5/4f87Vuo4Zh/4MAJh54Hljz4XzuOD+G7Frm3dG7xvP3IsVr/8udK8rCWX4FSHwGs/9e7ZjzrvzkHn8w1J7sLR9v6geuzfJdHhkDJux2v3YpzGn/TFkt7zpbBIrnDNpWSgZ+Ibf1PiDL+Vlv/keZu/8FRpa58XvjM3w243vrFXfld7FrJV3RTq03/iQHfdT3uy1P3S8b3tnue/2c9ofw+7HrvEsn7ns3zDl5U9J9aFSUYZfIQ8nSZo5EJuGbFrbYKnH/ojO9Jwncsa+bUbPSztueaHYE7hMZI1QIvDCOUMM7pIhoaWZ/YkqelRP2ZD8foL6aN40Hdu4zmNar80ZvYGGn4geJqJdRLTCtuxHRLSGiN4momeIaJBg281EtJyIlhJRa5IdV5QRm0ZsGp7Qg2I+Fy9zePzwDqCGSAkgfO+3XcLVupLCz+OXwfSAw5U5FOyrGkpOGn0UjSPwP4O7tkAZb3BFROZqfRTApa5lLwOYwhg7HcA6AN/02f49jLHpjLGWaF1UVDKmxixv+E2pR2yw7Bdk3uMPH5oZR+qJXv+1uPiFc4YZlLQbs6i5euwaf8mR9viDdhN809RqtN5C4K+FMTYfwD7XspcYY+Zc9YUAxhShb4oqQDcqUMkaHtOf8ou4cIYK6t6bBO/C9wzmug2/VPcAoor1Znn9ihLHr9lvrAlM4Co9yYRzuov18AhVErKKSELj/ycALwjWMQAvEdFiIrrObydEdB0RtRJR6+7duxPoliJpChe6XeoJ9vi5xVR8pR7d+drtmdmn7IuMgEceKo7Gn1QhECk4hqoQ1SNxKUepbyvcVfkkEOknsqB2EvvRKtQJiEssw09EtwHIAhAlAz+bMdYM4DIAXySi80T7YozdzxhrYYy1NDU1xemWoliYhsOu8YeIKgHsedh9pB6Hxs84bYPjuOOkZa5Yjd/H4w8zgcth+GOFc5aJhCZw8da7b4rinFPVTWTDT0TXAPgAgE8ywRlmjLUb/3cBeAbAzKjHU1QmhQlEsql+JTx+uwat50Buj58rEwXF7ct6iSxc/dcyafzmU5E1uC41KSvf1yS82Pz5rWypx2wnOjM86cz9BKmkHhtEdCmAWwBcwRg7KmjTSET9zdcALgawgtdWUb2Y1bJkB3dlNH67Yc9r/C7v3bE/gRFwh4DKpmyo4Ed7z5MQABbmiYtT/CTyAwtnw5LVH04onJPXX/dNotdKPUT0BIAFAE4mojYiuhbATwH0B/CyEao5z2g7ioieNzYdDuA1IloG4A0Af2CM/bEon0JRUnIHd+BQR368/8DW/CxNv8Hd/W3eEoR2L94s4n344H7s2rbJlQCM44Eb7zcsew0DcvvBxXbRt29ag6xkhSV+FJFP+yP7sH/3duH6vTvbsGHZ68L17yxfiD3t70r0S8fujYUJR5bhj1APQCRfmOffpKe7S1if98jBfY4byJ4dW7Fl3dLQfYnCwI61vusPH9yPPTu2ovNg/jfajx3Gnh1bPe0OtvE+m/NmkWZZbN3gP9GrGgmscMEYu5qz+CFB23YAlxuvNwKYFqt3igqjMFNz1z2/Qdd1f8GMN75mrBEb/mnzr8e28VMx+oTJlkG259JZcv8XMOtLD2P/PWdhLGvHnksKKQB4Hj+xvKGZ+Mz7xT21bTPqsVlYlz5J8iNyooh8mL35Z8DPfiZcf/C+92Oivhn7Rq7C4GGjHesOdezDib+9BG00ArjD35i9+cy9mLn8jkI3zcpbESpwiaJ6zPOPufk6vkvuuw6z9v4fdx/DHpmNI9THej903hQMle5BPE7J+heLNz+H2Z8BOALMm+Jos23jSrQs8RaoJ8Ycbv8I7AZ+eQ62fuo1jJ04NW7XKwY1c1chj81IDMM+HNq/o7AqIJxz//aNAOyDuwXjM3Jfvvj32PyQkKvIB0fqYTl0HT0c0FXnNidl1/m2L2yYsxJ2JcEEfTMAoOuYt79dx44AAMawHZ51nm61L3G+d0s9UuG0/hkxzfNvMnrfQm47AEhT5Uog7s/Bo2OX9wkAEJcMPShoX60ow6+IjD0OOkjjz3WbUovX8LvTHtifBpiuhxxstToXfhsA0JM1/CbcMYZQfeR79OZ0GimP3+iClkBUTxC5Kq1dK57lW1sTuZThV0TGri+zgKgevcfIqWNeWDaNX4M4aodBFMcfkMYh6sQkPQdNL4Lh5xgOXnim/P5Mj9+UeuQv5aRCFP1Mew5JFXQvLULDn6vcJ5woKMOviIw9KiLQ4+855t7YeumOnHDYbF33TthhuteDjpON07FhDpqR0z/S5gIvnptnRyJlgIVLyjE/n5WkLUSOfbvhjzcPQbxttkoNvxDl8St6K25vyJFTJ0BjZm6P33Yhebwsu9TDeFIP89Z55dwcIqHrSMXw+IWG1HfyVYzjWE9dMobfP2lZklRrJSuhx1/CSmuloDq/HUVF4JhQFGD4dVc4JTmkHp+UyryJQpxIH6/HH9G4MR0pPYbHLzguLy+M9NwCAN6aBGZ0VISoHsf5Fvch6Abhl5I6VEWwCiLMZK9qRhl+RXQcWnyAx28Us+bl6nEbfj1nL7bO8fh5U+3jFPx27DsX0+MXHJeXSz+Xtb329/7dN9ZCHL+81EOcCVxx8D9itRp+/xQgteL5K8OviIxT6gnQdLNuqcfH8GcLHjdX6mF6YEqGyB6annMWKgmJyOPnpT+2D/h2dXInwBdwGXbr81k3FPlLOZVAds4gqjWLfVDSP95YTTWiDL9CHnfYpR5C48865RO7R+6+2PSs3ePmDO6CeZz+oApc0rAc0kXQ+Hl6vl3+6Q4y/KJzIJbaAAAgAElEQVRwzgiDu47zHcPw+z05lDGFWyyCPP5QA/IVTODMXUV1svTl/0XfRfdg4jcXQkuJvfEF930RlOvG7H95QGKvLgNtC+cMTNKWzWv85oU1a89vrVUpl7fe0vr1whF1xtHzgWO/ucGxiHdziMKc7b+MtJ11VMGTBk/jP/T0V6zXfh7/gkduwZxdT7qOk99fw+qn8u8NQ7tnx1Z03fdebJ34KTRtfBrEGI5eeBemnHOFta37fAPA1vXLMNa1LMh016LhF01Ms5IRugz/gge+Auo+gtlffLDofUsSZfhrlJNeuwl9qQtHjx1G334Dhe0Khk7G8DthOXuemIAL3TKIXmPhlwiL6TlPFSRiOc+0fZ4cVA7CePxTugq5bXq6xIZ/zrve4ujMpdd39hkBAHjnL7/ELLYTo9f/2Grb9srXgXOusM4JL5xz+ws/8hj+IPznA1Sn4RdhDsS7nyTnbHvUeFVdhl9JPYrI2Acn5VIG8CNB/AwIYwzEJBKRBaR5SBphRItQOvF/AgmWetyHYdx+8J44Mq4xiwxx5ApeWceAPjSQeCyk1Br/G4M/WNwDGI5FLhc+KV4logy/IgQuIxPG4/fBTzIg6N66p7w86rY2uq7H0q7jIPb4/W9E2a5jvuvFxzEHy8WfNwO/wWrzBuI1aHF89mqVekQUNH41uKvoZXi8S3tOm6DrnImb8TRnazOdY/i5Nwp7jnm9bHHXouMG9cdP6uHvz51wTXwDcHv8PLQI6Z39qHjDH9YxMAveKMOvqGVkomIcUo/0Tyms1CPr8bt06yIbflEUTVSPPxfS8BeKquQhnzGUesvjFxdPIa7hr9agzGDChrFaYzQ1EtWjDH+NEzVOW2Y7h9QjGU7Ik3VEqXCNjkBzyxDcCVzuOr3JGS1upSZhrV//cEARheylIfFIPt7j1FHW1daL1DhKmG5VuMcfNtum+VtXHr+iKiiu4bfNsI3j8ZNPWCDH4+fdPMgl9ZQrqkdoGII8/u6wHr8g705YT9aqw8szhNGNd6kNf+jjhTTg5m+9VuL4pa5WInqYiHYR0QrbssFE9DIRrTf+HyfY9hqjzXqjQLuihEQ3/LwLw2dwV9rjD4muIxVycDfpqJ4w5zDMzF077lxG8sdxa/vRvm/P+Yuxr2og9BiQIYXVSs4eWTftUQCXupbdCuAVxtgkAK8Y7x0Q0WAAdwCYBWAmgDtENwhFcUjC4xfp0+HqvUYzJozp0BB8HHc5wSRTEYTaV0SNX++OFtVjafzm+fU9DqdvzPT4beM1CZy7Ss/OGXZmtzWBKyCnUrUg9e0wxuYD2OdafCWAx4zXjwH4B86mlwB4mTG2jzG2H8DL8N5AFEUk6kXsyLwp2oeeTDinH7ltSz0e/9D93qLeE7oKBd2XPfNjpPdKllqMwJ4dWzC5exl3HdNzWPjE//Ms71jxR27Bb5Ohax8PZYwsz5MVPP72zWvRsONNbvslLzziuz+71LPk+QexpvUVjGK7pPvjpbI1/s6lT4Vqz3o6sfB/bseWJS8Ftt2zYyuWz382atdKQpyZu8MZY9sBgDG2nYiGcdqMBmD/tbcZyzwQ0XUArgOAcePGxeiWwkESHj9zywnG2wiTWcJmhhywf6WnQteJuY2edn2pkAto9oZ7QvfLD/fjffd97xO2XfvKo5i99oee5XO2PYqt970E3MEvFH5ibhO2vrMcYydNk+yTc+YugWHUozMxStC+edFN2KiN5+0ov73N8Lcs/oZUH3z7F3sPxWXmgedDtZ+68SH0I7mnsmP3XYyprB04ryNK10pCsZ/HeLd97m+CMXY/Y6yFMdbS1NRU5G71IiKnJy58Tab3742usRvkoEs9/1Pzy+Hupp2Gg8C8Gn+JcT/xjGI7hW31g+LC6aIi4IuGfhgAkO2RTw4njOP3oa9+RHr/cQmTNC4Rinw8WaMPyBV7LzdxDP9OIhoJAMZ/3nNhG+BIATIGQOWflRoiEY2f4xUCCBnTHFz9qYc5E70d0/oirXch5a7JW2JCncMIUR8sVWdsGn7bQpprieNwFxbLN69sqacUVHLu/jiG/zkAZpTONQB4otaLAC4mouOMQd2LjWWKEhE1/Mwub1j78KQ+tpVPlH6y8DH8LuWxhxqQYd1IIee5KVQq3jKRwbBUJv8/1GC5W+qJZmQK0UbJGupKl3qShmfki1XrIAlkwzmfALAAwMlE1EZE1wL4dwAXEdF6ABcZ70FELUT0IAAwxvYBuAvAm8bfd4xlihIRfXDXXf6QE+sdSurJ4zdZq4echj+bqkeG5T3+cl5CoUL4Ihh+0vKGP5THb6Z5Foy/8PCXX5I9w5U+gStpsllvWoxKjvmXGtxljF0tWPVeTttWAP9se/8wgIcj9U4Rm1JJPY6UCbouvOz9ngx6kHG+T/VFHetGmuVQTukgzDnkpz7IozPielqmx6+H8PiZy+OXM9yFc6gzgkbJhr2KjlUaynujyWV7kKmrdyyreo9fUc3EN/ym5+Ix/Dm7l8OJArJRqPcqJusy/HqqAXXoRho5iG8l4TjK6oMbxcHHeAu/Cc3wv0J4/FGMin2LLIornZXe4y+vke3hDMxXssevDH+tw6n8JAPP4/dIPfYftq29X85yPy0665J6cqkG1Bsaf1KGpIvqwm8UagKXT8I5wWcgy+MPY/idg+UyYyz241uGv4K90mpC50g9yuNXlI2gVAFCbEbI1Ps9OXP0wo/dHq3DNfyi3DL2Ji7DyNJ90EA9SBFLzvAjvMeflNQj/AzW4G6YCVxOiUdufoTN8LtusvxcPdEpeThnmeFp/LUa1aOoApLV+N21b70DwIAzXbOtAQD/FMyeouuZRtvWyRiSnggef5jBXb+oHvPTdR474jAKFCmqxzyevMbP8/iFT3Mx6W2Duzmux68Mf8k5OHcUln//AuH6BY/cAswdGGrSTDUS9cfnuGHoOWDuQG+dW52v8ed4WnXQDWjuQIzJTwQvkOlj23syhqRbawi9Td+7xwFzBwJzB2LRTz/n29Y/nJOw/d21aPjBKCyad31hqWX4Qxhf15jJtGNvBG5il9lyhuGf/NLVwNyBmJTbIH9sCTrqRia6vyD0AWErBidLLuu1I0cPHgDmDsTCJ76X/z/vXwAAXXcMxbIfXFzqLjqoWcM/AEcwtest4fppm/O5S7pDlryrNpLIzilMNWwzcnZvnR+WmF/vV23LQ6ogRySlluYMiWM/+mNd+qTQ28/a87Tvej/Dr4Owf/smAMDEXYWcL3E0/jBnRuMY/mKw8ORvQDvnxvDbDfto5GPO/OSdkbc1ebvhzMjbMs5Y2r4dmwEAo9f9DwBg9o7HAQD11INpxxZFPlYS1Kzhl6WSB2ASIQGpJyd4KrLr2c5wTq8BM9e78+74QVqcVFJ8TMO//rjzrddJQj7Gm4GQM7Jw2g1vFI/f/HrC5D6yp77QqXiX/vhzPgYtlQluCKDdluKL9Rkc+ZipdBoH0Tfy9gBwZMiUyNvyCrvoZg7/Ip7rqFRejxSJEl3qKWyXzfFrtjqNXNDgrqklh+hPEQy/ToaRLcK+Af9KVgyEnPGEaR9cJSNlQ6ikdxHy79tTX+hF9PhTqXSI3DnJjQWkY49TRHcCeR5/Yayr8sY7lOGvcSI/0Oh2j19g+Jnd47cNBicl9RTD4zf3SVpRLsegsEqz0lbOYfiN1yEMlzsfv1Tf7HJcEb1Q0lIgScOf1PwMIORvK2H8PP5KpNcbfiX1CDZzaPYCqUeQsiHnE53iF9Xj2X+qGF45uf4ni190DANZlbZytslqmmZE2AjGUnTG62s8jb+YHr+maSDp85vc9xDmt8Unel90jsfPciqqp2KpVcNvendJhHPyQtUAsdTDDee0NP4QFwMlb5wKNytWlMlLQVKPWWkrR3aNP38ZijT+bp/MKqE0fofU4730k0qEF0bqSTLss5wePy8rq+kwVaKF6fWGXxixUiOEChG0YY8wyWX5xswp9di8Sa6nY8bxh/BQi+DxhxpjSHj/DADL5j1+3X5TI9PjFxh+8g6UmppymPoGacfgrtfIJ5XGgVyGn//Ekjwalc/E8iZKMqXxVzA16vGbnlTkmbuOmrt8w6+Jwjl57Y39pSmMxl9Mj784F6NfSgpGBNaT9/h1u8ZPQR6/d9KZN0lbMA6ph2P43dlRo5JKpZwT+nzOdSVGvESB993xJzJWBrVx1uNQwbPrEiGBqB5hOKdocJcXzgkWutAIFcHwpxg/zURS+M+AJSCbLxFplzhI8zf87uR1RmPzhXzfbIafcS79bKxKrAVSric1/8LrFeQNx0gzwdf489dHJc5iLk5MWxVgfhk1L/X4PNG89eJjOIOz/M3/+ylIS8GceykKM9Rcg7vHjhzC0l/eCsp1w101eea+32Hlwj9icpjOF8EbTLPiztQ+pWeVcF0968acrQ8CcJaxNG9wk5b9EIu6j2LQiWfi5JYL0fq7+5BtewtjKO2x79vf/jPGTpoW6uaVDvD4kzL8bonOz+N39L7Knr4XPvg1zDZeH/zjXVj8UgaN538ZpxjLmO4dG+vu6nQ8vy3786+R6+lC8yWfLnZ3HdSk4ddzOelHmUrOp5EEftfSGQv4syvPXHqb471ocNct9Sx98i7M2f5L4fEmv/wJn556sXv8K064FkO2voTB2V0YigOB267JnIb9I8/FnC33OZbvGn0RTtxyv/GutIamngrnMWUz/IPHnAwAOA4HMWv194DVAFo6rKLn+9Hfs6+Zy+cCH/5qKF/SroHzJJa9mZEY1iNXJ6mNRmIA68AAHPWsc3v8/h4vYeGkrwEH/SuyvjF1Lo5b/Tj2nfQxzFr1Xak+FpvZbQ9Zr8848hoAYOGiodYynsP01nM/wyzb+2nzP59/UWLDX5NST09Pl3TbWvf4k5CyRFolsRw2acdjZd3p+TsMx8OJhc0rrR82ESd9+00c0bxG0M1RVo9TbluAuhGnOpYvO+8BUJ/jCrsvY7yFOYt24cSbUFfnnz8o7RMpFPXmxTgef8foC6S3bx80Awc/9TJ3naY5zYrb8C88+RbHutmfvAOzv+C8QbuZ+eGvYtK3WzHro1/HG9Puku5nGORDUH32YX8K5sXxJ32NRCSy4Seik4loqe3vIBHd5GpzARF12NrcHr/LwYg8VD7V9XgZmgQen5lgIorGctBJAyMNBJbIhWOHNNv+rMlcIVIka87+kMPLLe/3bqWuIA2U8h/LyMCnvkHU0pq8UNm6Pt5lwgOTMHCANM3xu3M/f9uf5KLp38XRzCMHQtixBTboVuK2GtL4GWNrAUwHACJKAdgG4BlO078xxj4Q9ThRyArCD7lELFRSLSTxYxaVBEyxrDVISGBgSWvy9lh3Y+BNxku3jInLuJFWOQ+4ltRDmkcacZP2yW8U1aTwBncpEy/XjfBY7kFT2+/EuU7utyo7K7gc2PNXsV4Qx/9eAO8wxt5NaH+xEEWh2LEGd2te45f72fkVjfAb3NUplTcijMWKiuBhv8DNWbwyHq45mOgxEI735TUelnxDqcAZyv4hsMlJPan6Ihl+17l2RmvZ1lXZ4C4PxwQ+wYz3SiApw/9xAE8I1s0homVE9AIRCYM6iOg6Imolotbdu3fH6kwuRI6MWh/cldX4/W4QvLJyQF6uYNDAiIz49aSlnhT3dRDmk4d7G3I9kZRV4zfkG9K0fNx7RKJ+Bp7hJ8mMmkA4icadHiK+1FMcEtH4bSG5pkRaidXIYht+IqoDcAWA33BWLwFwPGNsGoD/AvB/ov0wxu5njLUwxlqamppi9SmMxl/J5dGSQNaJ8ssFzwtLA/IDlPnoEMoboKQ9fptGr4Ux/JztjZ3Y1pbXu0zbNP4gqcePJA1/KFmQSNqp8IRzVqjGnwSaw+OvjIFcHkl4/JcBWMIY2+lewRg7yBg7bLx+HkCGiIa62yWNKJskj1r3+GWlHr/oJhbk8RfJ8Ds0/hCG35wwRK6ZqOT+uZdRWjA9fpBWlrEHxjufoSbYyX/XvlJPBXrDcXBG9dS21HM1BDIPEY0gQ2glopnG8fYmcExfZKQey++r8cFdeanHp51wcFfPyypEhsafrAHTHFKPofGHGNx1G9T8++Jm55TFzJtDleTxR8zrFIRn5q7Q45f8LEW6WSQhyTiKE1l2qPJubrGuVCLqC+AiAE/blt1ARDcYbz8CYAURLQNwL4CPsyKnwzx25BA6dmyy3ptpArZuWI5d2zZ52tdqdk6TQzs3S7Xbvzs/gWbPji2edaI4fg05MNLAoKFBPwo6Kjf5RxpHBkvzdXJRPeXU+PuSMddEYPjbN60J3MeOrRtiGH7vpc/LKe+7D9nAAd/B3dqif/euwpuAmP32zWsd799duxSrF71YjG55iGX4GWNHGWNDGGMdtmXzGGPzjNc/ZYxNZoxNY4zNZoz9PW6Hg9h6z4WY8qfCLLhFv/gWmK5j7C/PQcMDZ3O2qG2pZ/rrX8D+3dsD2414aAYAYOi8qd6VIo8fRlQPaRjL2jF7p2h8PxoOjd8wVDLZPQsevzeOv/+4aQCA1LiZSXUzHpoGjTO4O+qxWZzGTkY8NAMNTH6yoh3GS8gWwuPXxs1Cv0H5sbijrN637ZaBMxzvB40pTKyz3xT6jHO2C8s7qRNCtT/C/CfOReHE3EbrNQVIPaMedf4Gd7z4YzS9cF3ifeJROYHNCXFSdp3jff8dCy3PZACOeNrX/MxdAEcPxfPERdk5U4bGr2ve7JE7MURq32000nq94r2/cKwju8duGMc0k5HxzHBOl0ElwuSz34/tn3sDLVfcYC0+iEYAwKJTv2ktO/CltWid8UPhMd4ceKln2Q6ED0rw9FHA4v4Xcpf3o2NoHfA+bNTGC7dddv6DnAM7L/3WGT8MlHoWnnwLOr+xDds/9wbOvPJfMHjYaOz85yVYPvnrVpu9X1gJwDlQPOg9NzrWn3j6WXhz4CVGu0I/pl/0CbQ2/7v1/q2+Z2HHtYvRceMGZ9ddkoy5ftTX5mPHtYuxsm6atW6z5s4aVYDdXHiq6viy025s/tgrsW8MxHuC8nlKIpYLSGiXHDVn+N0wkG/ESs1n5wQQ+2sWjJmkWQ6MUtBTXo/vQFrOCHZkCu2GjDvFsc4uzWiGxp/2mcVqItT4DWM38viTHcv3avk+UF0hjn3Q0BHINA4UHiM37DTPsn11IwL75kZW9silxTH2uUw/7BkoTn/Xv8lr/Nw1h+sHNAV6/JmBw9HQt5/j/A0fc6IVBnqENWDI8DGe7exhtOb6XP8x5kpH28bhBa89mxmAEWMnYuBg92+psE0ny1jr+zT2x4ixE3GsodD+UEYcS9JvQCF9x8Ahwx3rho2dhH1a9OLvgFPvN/EbSyOmK8OfHMTVIs2BHF461Voj7kxHkSeYMjR+Pe31jOTzrBfOfyrtfHJwGH7T45cw/FZUj2dw16X5m1XKBPtxRwW5duZZFCk0UTKG33dWNGm+xeO5UUPu/VEquhNkppUWfH7e78+8/tznnkIO9vKPaZv4Z0i5nUx+jgKQTBEg0/A76jD7jYtY4dHFp+YNPyNNefxxoxUEWmUKubzR4Xj8vJQAnt0y52XuvtjsnqIZ1ZPxTVhmHtuUetwev/s8eM2O452fUeYZ/ggXrbuPPg0D1ov7yjW8rhsFaRQjqodvxG07F27jiXgJ+VvlGX7792DO9O4JmZ0mSqRVt6t0pSn1OAbglcdfGpjL48+60jnUehx/GIST2YQefz6ck3E8fhkj6E7elcm4Pf7ChWR6/HWQ0PjNvD5uD18QLy8K4/OTYXjronj88hEu/qmNmSb2aLk3F3fEE6WAwKge/vmzKojZzmNgxI/UDU+wD3tZR16f7Ibf8PhzUmMphf1GMvyuSmlm+oYwhl/GYUqCmjf8Voy5QVenM394rYdzAvJOlHCgWzS4SywfDx7Z8DsvRi3tNF4Ow2+8TknVVRUYfvcTQMB3Tz7ySWIev98xpHdCtuyl3IMELiNNCxXV4zk+wkk9wh+lPT+TTF4m3tOMrR9mzYiwRWZ4kVZBdJHL8PPOp6/hV1JPYjA4pZ6uY67Inl7g8ctOUhPWMfBL50ApEM/wS3i/bm8t7TH8Gvd18H5FE7j8L2a3gfK9+LkafxTDL2lgfAwCA/Fn4hpo7tQV8M7c1bRUZKnHPG+JyBQy2TrtTxa835nj5pH/TG4noxi4PX6N6/H7afzK408Ocko93V3HzBUAesfgruzEHFGOI/KZiMJIAzLePO68maFu3Dlc0m6px2bsNJdHm2N+5fzMlA1ujT+wS872flIP14suk9RDBPKRerg3DfcTgqYFPwGJumDtX+Txh9lZOIKkHtPjzyVUSN6PHs0t9eQcfQCConpUOGdi5MM5Cye7p7P3efyycxWEdQwEUg8AgFLQMhGlHtfNwa2rOjV+5zq/R3exxu+O6vHHd+C15B5/kMbvE9XD9Yq9MljYmbvuvvnV1vX2STBeEHKcJGhw1ywwL+Xxx7wZ9RDf49ds9RT8bq7EdH6BnCJQU4Z/03emcZa6PP7OY461ojvw268+hZXfO89K+VAOFv78Bix4+Btybf/3u3jjPz/JXSf7CC9Kvxzk8Wucyk1yHr/z5+eWVuwShXudn2/aqTV6tgdkImjcUo+Pl8iRnnQ/r1t0REmN3086Iz3rux/uzYV3UwzoiyhtM++8Os4ddzzE/DzOb9LRV5GRDJJ6bMu6tbxTcjQVXLKTN1YVhhw5z8+UrqUAgNG2/JUjtvyOu+3brz4FYrnkixkJqCnDn2besENGmsPw667JSKLB3RP+8iVM7l6Gw4eCC3sXi9k7n/AUCxe2XfcjzNz/e+462dTTwgI2tpvjJu14LBh1TWEdaVyD4GeozFmofo+1yy981GEEUob+v+Efn8eiyeIKnouGXAn94u9ZfbOWD/0wRp3gnuTk/e7f+dAL1v5lpZ4ljedhwZh/QnbiJcL2wv0YN6dVlz4Z0JCw7opn8Vbfszyr6o5u9xjy1v7vtW3q/S4o5Z0zccZV33Qs011y2ukXfpzfNc55OnkGf6ZxoU98eWjcKTNwEP4FYexPBXyNP7/v7WhC07VPYsG46zD6xhexcPjVWDjik3hj6p3W+X77/Iew9OyfAwDOuOpWLOszCwtP+rp3nxJ0NE4IbDNe9+bCAoDxf/0yCKxkUk/xha8ScuyDPweeu9K1lByDk6YRZK73brKGx5rt7ky6myVHNmQ1K5iha596vvPEj6Ch6QSg/bH8vinFD2308fjrZ34WeOXPvj/ySWdejJ1b1lvvTQ9y4rSzMXHa2ehc8X3udrO+XEj7YBq8zdpYzPrSw8Jj2Tnx9LNw4ulnOY7Jw/6Zm7+e9+Jan5vnabeifrrl+dk5xPqgPx2zPPXTZl+K7hfSqCOBrEYaTmq+AEt2bQYWulNekcPwv3HcBzDzK48Dcwcam3KMo2tchkhDQ99+jmXLGs/CGUdfBwC8k5qAE9P882He+O2DmKRp2KyNxXh9K//zCAbrG/r2w4oZc9Gy+Bvhymzalxm/vXdPugazR4zD0H/6EQBg9he838/p7/lI4dh9GjHtlpcCjynsS4QnPmtbUN7jVxp/eNK8YtHk9Oq9sgf/x2VqyD21YPilpR6+0XHMPPTM+BTkk/fTSy1N2H/SkV2qcUf8JIHHsMSM6uHNwg0aMJUPGzRCJjnBCIxSTufGM6jNkWJc10rQTFXNb1DSLIspKr7ODef0Gz/hy0A8uB6/eRMsUpppYV9iyDQ6NGhmmvMSUFOGP9PAe0R05upxe79Cjx95Q9PtivuvRmTHKXJZQTinfdCPyGFIGKU8A4VAwECnafh9fuREmiOBWcoV8VOKkn2++jvnZueb4kF4jLCDed7fKyPNWSXNZWh5N2bN7fEHhMtqPgXfNY7Hn3/vg4zhl9D4+VE95TH8cQaHdWgg6CqOPwqZet4go1Pjh8vQi2SQrHERZ7uq3/DLSj1SHj/IafQ0frFwP6nHjOjwk3qIyGEUPTH+ieTSdxmqEB4/f0Az/OUknRPGPB7PGBK5jFzwoLZW3+h8zzU4tjxKPhE/WpDH76PDS7cX4Cf1RI5Sikx0w29JPSqqJzz1DY2cpeQwfJ68PQKvwoz77ek6xl1fTfCeanjLcoKCK3aNn4icjo1A4/fzfkzv0s+70bSUwwtN+Ug97kFIWThDnq4++D+ReBfyLtqg2cFy9WcLSc04+2PMGXIbmKOII4sGPHloPnUrChq/p2PibSQ8W/HN3f7EyZujUC6PPwGpR2n84anv45V6GGmAbtf4zcFd40IK9PhrwPBzPiMvmskd8WTimHpOGpwXXipUIfT8PoJneuY1/sJ6P40/TPy44Gj8pSEzXkapLOUoL+l7kxB7/ATdd64Fr6+peue1wpvda3/S89P4zZsyidrwnAC/cxVK4+dQLsMf83dI0KvH4yeizUS0nIiWElErZz0R0b1EtIGI3iai5rjHFMH3+J1ekvvxTxTOacbk1oLUw8tOylsmCuckl8bvuJBdkowMZHmvKeETV17qkUvZEN3wxxh45Vyg/PMg7/H7NzR3x/f4vTdn3sYF3ONhvIIwmi0Tqq/Gbxh+d3U03xuZr3csb5a4HrL5WUot9cScAEZMT6TurwxJhXO+hzG2R7DuMgCTjL9ZAH5u/E+clCDczDG464qKEA3umlJPrrv6PX73uEZ+EecpQCT1wKnxOzRYLRU60Zi5ve/grqYh5bNfuyySv/gTuMhdF51fhkaewY7k8YeM6uF6/Ex31Hd1S0Y8ycot9fD6bjf8Kd/BXX+Nn2fkpdJRB4/tCqJ6jH2XOqonpsevVZPHL8GVAH7B8iwEMIjIVm+vyAw9vBYbXynEcB9e/zfsbHun0EDgceqGx5/ryRv+Fa89h327thWvo0XElHr0XA5LXngES154hBum2vEmv2bu0GObC29cnnhe4w/5M7KiegJ+5JL7jTrMGzRA7J+WmSONcG5UgWkhZG+a5CdNMi1J2y4AABydSURBVGe1J5lwTvcsac7AtD3HjJ/Gr4lm9PqFsvpGdJmvJKJ6uBq/0Z+Se/zRzekw7Ks6jZ8BeImIFhMRr1LwaAD2WRxtxjIHRHQdEbUSUevu3bsT6Fae8fpWzNn0U+v9nK0PYOADhQcOJvhB54wLUu8+Bj2Xw5Q/fRod8y5PrF+lxAznfPOpu9G86CY0L7oJb//6Lk+7WXuetl7vQqHs3BhmL9buknq0cBr/wmEfk9L4gQCP2y7fCczr4JH5mZQ7T/2sdP/s9BtwnLByEy90c8Dw463XqzP5WcJHp37a9xh2AywljXAMP4GhX/NV9s4ByNesPcbq0KffACxpPDegH97P01lX+A2sPeFzwm1TltTj7Fv7qf8EABg8glP31uemPvI0YwLd9E/w1586x3rN++5NR4SbFjkEZv+liSnTTNA3l8zjT0LqOZsx1k5EwwC8TERrGGPzbet5Z8PzC2eM3Q/gfgBoaWmJHKv3xtQ7MXP5Hb5tGqgHx2Dk5RBIPabHz3o6kc32oA7AuNy7UbtVVsxxDb2j8MSSPiiYUWmw6dQbMPQj/wrtLmfd0fxFZdPeKSVdPhBzOzAbwJpF+dmRdu/m7fMfwumu5nUNnAl5HESGf8CgIcDcDnld0XXhNvTth+5vtQHfH+5pmuJMFjz+5OnounU76hv64lRjWQsALBbnW7JnHc2Q2FCZ8hhvTIqYjslnXY6FG2/C7A3/AfOSm3bz7/O/3foGNH/dmc6DVi92vK+r9wZGZNONwNwOAMBsYc8KGr/7W5j10a8D+Dp4WXL8pJ6Rx58MzO3AdJ/1m656CRN+c3FRNX6z/+YM6KXnzMP0124AABy8aSMGDBqCPTu2Yui8KeaBYx0PiDcJLAyxj8IYazf+7wLwDICZriZtAMba3o8B0B73uEJC6qyip1Ez2yHrOWalK3ZXjKoaODc3CrooSBPU6nWGczIt5UmZHNyd/LEdP3LOseq5E/KM49ousqj5TWQKfdTV8xN3pTlzRgD/PvOQjf0XJTXLLzK+X/N7Ns6rlkoJ+++GFxEnjNJxkRJM4PIlpoErpFMXRwzF9fjd2OU9c9wkbj1rN1Wh8RNRIxH1N18DuBjAClez5wB8xojumQ2ggzGHdpAo0jVMTQQG0Mq02NOFrGH4S6W/JQ1PFya/VMuApeW7Y+SJnBO4iIg/QOljVM2wUZ1SlrHgxpq7ZusK91eiSAg7aY6HHAX5ot5GqmluOKd/0Xj+7pznjB8RJ7dHLR0wuMs7fISBcDtmGhLed2+NmySs8dtPvfl7dcicSXjrJfL440o9wwE8Y5yENID/ZYz9kYhuAADG2DwAzwO4HMAGAEcBiMXCBAg70CgK57Rm/2WPWWGO8ePFy4MVucSpTBS4rfs9kTOXeoSoHjOyipFWuDhjGe+EvpcQfZCVoYKQHh8x69qKonoAy/OP4jXyI+LkDLl5g/YbAPYQNiDAg3mzE0/gkv2Ny6L3FAIizO/NHjGVhLdeKqknluFnjG0E4EmCbxh88zUD8MU4xwmF9Inzn8BlXkyUtXv8VWr4eR5/0EVhRgJBc4TyEWmuOP5U6PqkzHjacF60cWY9xvteonyvGcGckbCEL8QimMAF29OAzMeRmTkbWuqRJ/STuQv3REzHvos0gUu3lSa1nlAdcmX8Y1SF1FORSHsSxkUimkBkGEbKHoNuxLeXKld24nCMvOZTXAWAddF4Lyx3ArAUN8+L32N/4THdFtES46KJekOOk++nTqDxh8UvcomHX20FK0KtRF6jiTWBi0JIPXENP/M+xRY6ZHr8yVbX0+1JDK0qb/nPkWVaVUk9VWrJxNh/UFkm8/EEPw7L4+9ELmtIPWXQkpPAmrRmu8lpAR6/eWF5jCqRc/azlgKF9fiNG6nDu4lxbqM/iblnmsrvp75PMh5/kMZv1Ra2Mlbynt5MqcdnwNONhFGUNZyZTJTKY0X0+Kk4Ug/zkXryTmECUT0xxz5kqTnDb8e/zqY4vzlQ+NFoejdyPdUj9fATshlPL7YBXfusTC6mV+7J9EjOKCEqXlSP9P7K8DMOG70jIsgA9sAcODXbiQd3TcMv403LZWyV8+D9EugJiSlpFBwTn5xJCXv8LOuVegqGn2L9hgs7VoY/EvYftFT4pVDqye8nleu0slb6JaoqB91dnR5Dn8tlcezIIWdDU7bKFX64QR6/OCKCnNENWkrgtfoYDd3r8YuKbxeToEIsfshGHImPnUcUXGBiFgSywjkFuXry/3VnWx9kivPIevzpdPhzUbg5RZTbfDx+c15J4h5/tpDLyvT4ze8vn08/CcOvpJ5o2Axh1ufuaf7cpr/+BRw5dACYOxALHvpXa73l8ee6rPDD/nQMC3+Znxy24LHbgLkDke3pxorvn4/2Oycl9hGO3DHcmjQiomP/HtD3RqH1P6/G8u9fYC1vfeIu9PnRGPSlgpE/Y8GN2DF3osvwB3n8+fPYh1yJ2zTNeVPQNO7grt8gVV2//KSw7n5jCgsNY9WB8BJKVI9/T31+RmmWTMNVusthWyY/yzdti6bhSZM95gxhwyBkBgzztDEHd7UBI4z/wRlRRPeb/bbpVt2NowL3AxS83i2aZ0K+30bybTnUNeavj0ON3lnBDQPzE+56GpPNDJNqHGK9dodzbs2MBw0M8fkFVEVUTyVi92S6UYd8BKkX+715/842NCKfzgG421ifv5jSepcja2X/La8CuBPTNj4AENDddYxbUzUOjRRc7vHAzi0YSDmc2fFHx/LU3nXc9iOwG23Zwn4zev4msAeDMBTegvLCIhZEjqcMLdPA9fh7GkfgzRO+B0pnoKXrMGDkJEw01k0590q8dfReNF/wMbxz93vyuzUMQdfnX8eqrWtxmtF288deAdNzmODuX1DBbQkm3PAklr31Z+QW/wI4vFrYbusn56Pr6GFMfCafsmPVpU9a/ZNhyyf+inH/e75j2fAbnsXyVQswtf8ga9mea9/AiIdbHO1MudKUL8645BosAdC86CarjRnNc+aHvool/YbgzIs/E9gn8zrpZils++gL1vnNXvcaVr27Gp0H96D5gqvEO7BBmoYV7/sfjJwkTry77TMLceTATpxkbWM6BtG+uxOmzMLSHfMwZc4HPOumnHMFlhz5Kc44/8OR9u0m//0fQsuUOYCRFcA0/H0a+2P5ex7BuClnY9KgoWjd+ne0HPwTgHwWAa2+LwaPn4b6vgPQ9vyPMGvPb32PxVJyE+7iUnOG367rdVG91JOkzpnMZD7mpvVuK6rHOIDzcBKzP4uBqEBMKifOJqrpBY+/juVfb5j4OQzdcI+3sSjMFeRIc61l+giqNwFn/oM4iveMS65x7te4kIaNnoBhowtmfvypTkPII+rEugGDhmDae67CktZf+LYbO8mIWH4m/++02ZeGOs64k7zJBwYNHYFB5/2jY9mIcZOwA0MxAoVEt9ZNzRhHIU1D82WfA+yG3/T4U6n8OgnM3+3W9PE4cXIhqUXTqPFoGjVeah92ppxzhe/60SecCliJLOJH9QDA9PddLVzXfIl/jqQwWN+/DfvYzNTzP2S9Tk/9MPD6n7AufRJmfvgmxzapK78FPORv+JGuj9dZSWpO6rFr/N0kvnvazTXj1KQ19fw063IVKHEael5641IgqhPgZ/hTNqmnnuW9f6oTSCuiz0Xk0AnSdX24UT0y6RAqjUoI2srAGWZryli88pYmUcJShVFbJSJuVE+lQkYKjoJ8WEAqdDeTTJhwELV39m0Gq0eTu3tyPX7Di8owp8fvNmiyhcyTJiuoE5DOiWWilM3jr0f+tbv2qhU+KPL4SXPcXFP1ffk/6CIPhNuNXfww28q5SYkMv5VqmEOk+QjMnPlaJsNf4rkGpcLM2qrzUnRLhGpSpjRST+2dfWY3/HInMdvd5VlmDu7W6d3QbaP57otMFxQvKTa57rzH38OcP6aMLjb8ab3wOfoYUk+qwZk70QqB9dH47TfXdF2DrzdaChIzXhXg8tcxl+G3Eq75ePwRbrKWx18uA1yjHr8pg5rZfe341XA2obQy/JFwhHNqcmFmuR6v4Telnjp0uSpTuaWe8nj8OUPj73EN09TpYqnHflMwUwC7Uw9k4T/dnchZvD7T0IgUdwJXcb1obwWu2qAOTkdCN78Pv/q/Ec61LpyZXRp4pR5rATOtA8/jl5F6NE6672JQO1eMic0o6T6Px/YffLbH6yWbUk8d6/GVenjjA6XATBiVdRUEqffx+Pvohz3L3EW3rbkPouR1cIZzZuobyiL1OPsUs9ZpBUk97rQHpsfvq/FHOdfWU1u5DH/5n66KgZ41M896bY/Mk7GmNP6I2DxVnVMliUeOU4bQnOBUD7fU45owFZTeuEjohsafdc1OboD36cWkH/Ma/ozL8FuGRiD15D3+gnHK1PcNnZa5UqlEY8SswV3x02ssjb9cUk8FnuskMGf3Mq7HX7hO3OnOrTYJ5YAKouYMv12GYD4evx2dI/WYF1OGco50rJWi8Vsev0vqqWdiwz+AM6ehziX1FDR+n8Fd3R7V0xCq9GIx8CvaXu0wqaie8B6/bkX1KJJEzxl5vTi2x/5kLDrvqRJ5/DUdx+8n9QxBh/X6xL9/3Xq96Dd3o887L2CIfsRa1rj0Ice2rc/NQ4sxM1bPFY737tqlOP5kUcE4Od545l5PCTN737RMH+jdRzF9zU8AMqQe26/IPmNXBrfGLzW4azM0mTq+1FNK+SS2Tl3BTydSg7tRdmxF9dTuTbMcMDOFO8f22L9Dd7pzk4zy+KMx8cLPWq9Tp1yGjdr4wG3snvCslXfh9M5WjGY7cZTlw0FP6VllrSemo2XJLdZ7eyjocU9cFqPneWYu+zfhulkr78KZS7+FWau+iwYytETfRHT+HEQjho05wbHsnRGXY7M2FmMv/Rp/IyJMnHMlAGA/BqD/wMHQUimsqC/c8DZrYzHmsn/lb58wO9CEoxf9CCvqp+OtOfdG2seg99+BTdrxOHGWdxaonUWTb8fi/u+JdAw7b0wR14RunfFDvNX3LOt9zpAr3R7/oqEfwoLjb8C69Ek4ctGPQvdhwunn4J3UCUhf9r3Q2/ZmWpt/gKV95wjXn3TBJ7BJG4+R77/Vs07G4x82YXLcLkpRcx7/0BGF8r7jZ1yMwZd9LjDvjYg1/Wah+ch8xzKv1FO4a/OklGLj5+22X7MIO3/zVZxx9O/c9ZvPvxenZ+qwcKJZpBtA/+EYf4O7eiawvL4ZU7uWgAgYeNxQYG4HjrOtn/LNv1rnefzt3u2LReYLf8Fpw8cAc6LfdCdMngVMfjuw3ayrbgZwc+TjmMz8iOCmCqDlg9cDH7zeOpfmOJXm0vhnfemRWH3o09gfJ/7bW7H20RtpueIG4IobhOuPaxqJ425fxl0nE845eFj8fD8yRPb4iWgsEb1KRKuJaCURfYXT5gIi6iCipcbf7fG6G7aP8R5osnUDghuVKJxTVIDDT9/V0mnoPiGtKTN0LGuPHeffSKyMjwmH4cUd4qvEAdliIF+bV1GpOGcrl/d3G+fXlAVwM2NsiVFwfTERvcwYW+Vq9zfGmP8zdJGIaxT0TF90sxTqyFZ60GVoebN+i4Gu80Udvzqn6VQGuk/Sp7ShJ7KcvbKQ6GZZ3B9q1JS2tToD1I3GrYmrqFbKXb878lXDGNvOGFtivD4EYDWA0jynSCJdz1QASzegC06PWStTyoZstpu73K9GQCpTB91nJqA1sGvL2ikKs7MkpQoeCK1lEsn1rqgYyl3UKRF3iYjGAzgDwCLO6jlEtIyIXiAi4cgFEV1HRK1E1Lp79+4kuhU/VjjdgG5PsqXizdz1q6ea7REYfl+pJwOWEucrytTnbwqUC5Z6zCcBucpNIYh5I+ktUo+itqh6w09E/QD8FsBNjLGDrtVLABzPGJsG4L8A/J9oP4yx+xljLYyxlqamprjdAiA3mOIHpRuMnP62Ze60zAka/mxWXAA9m+VLSr5STzoNJuHx2wu0iAypNdGnWNlIIxrwmjf8VtiletJSJEcsy0hEGeSN/uOMsafd6xljBxnLTxdljD0PIENEQ+McM2T/4u0g0wc9mtvwuzT+BCdw5XwMvy5Yl/Ix/Kl0BvAx/HWGxk8529OEQDM3PZTEPf649BKNX1FbVK3HT3mr+hCA1YyxnwjajDDagYhmGsfbG/WYYYnr8WuZBvSQUyrJuLInJunx9wjkHACOKmB2UhDfeDKZet80r/V98h6/pgcbfpNyFZ7ptRjOi9L4a4tyG/44oQJnA/g0gOVEZNYe/BaAcQDAGJsH4CMAvkBEWQDHAHycldByxI340Or6eAoqjGI7He+P7NrseN959DA69u1E5+EODBo+Dl1HDzkqSpns2LIew8eciN3b38WQ4WOxf087dm5cAd4gyMEDe/HuWy+D96jkN3dA0zRfj7++IZ+nR9NtNzPRU5J5LhMuYB2bWpd6FDVJud2nyIafMfYaAmL8GGM/BfDTqMeIS1ypR8s0IBtQzKVl8Tcc7zf+x6U4rXu5Y1nHl9dh4JDh1vsNy17DxGfejwVjP485Wx/AgpGfwpztv+QadgBY99Dn0XLoFel+H2X16EtdIE3zTfOazuRvat0jZwDr/woA6D92CrctC8jaCQBbaRTGsnbpfiZBtWj8B9APg+BNkudHJ8tg//DZQNsq9G8aG7xBFTFwWP7zdAyfXeaelJ53tTHY3e9UtBx82bF8WcOZ8BZ5LA41HRzsLu+29OyfY/wZF6KnuxOpdAbHDh/Ewd1bcOoLH+Vun6rvg6xkTn8Tt9EHgMMd+xyG/8CWlQCAkdvyhdLH7viT7z7ru/eF6sPmSx/DkLEnYTjyNy8e7Z99A6OM17Ou/jds2/wRMJ3htIl8w1+I5hRr/IO/tgB7jhwU3sCKQTkN/4EvrQVpKcjMC6cblwL3TgxuaLDvX1YhXdeAmf0Gon3r9Rg94ZToHa1ARp8wGds+sxBnjptU7q6UlD03LMfQxgEYWdeAHTu3gjEdfRoHYm/7RpzMqe1bLGrb8LuknsamsRg0dERhwbDRGH3Cqdj5whAMx17swwAMRiEwKVXXF92JVL13GUvLWOX/1zGxtg8AGT1c4rU+AwZj+JgTAYgLO4wYWzBCpGkYfYJ/jhAZj7+x/yA09h8Uqq9xKafhd/yWAug/cHCofdun7o+qMaNvki/A3rsYOmKc9XqE7aYX5reUBDUdEuEe3BVp/mYirB44M+ql6/pA94mDl6Wny1UVyzCempGdrw5Bhl9cVYvb3lZHN1XXl9uGm0Pfj2LF8Uek3INjYakWSUrRO6hpw+829KKLz6xi1eMayE3X9y2O4TfIGNE09QEef11Ijz/TUPDyU3XJ1PAsehx/SCqpapYMbtlRoSgnNf1r9Bh6kcdvKF7uMoaZ+j5gCUg92S5+5I3p6ddDHL8PAPVMXE6R296WYz9dz/f4o1IpHr+J8qQVivD0KsOvaXwjYUo9Obfhb2j0nfkqS7bLabjNUo6mtu+us+qm3qecIo86m8efTqx4c2Xm6lHzChSK8NS24Xc/Xgs1fn6lrrqGvr5x8LLkup0eP+vJSz/1Adq+SR+fcoo87B5/UhV9CrVZK8PQVpvGr1BUEjVt+N1oIsNvlEkjl/dY19AXLB0unJO7f1cxd2bUy7Wne/ajjsKlhUjZUvim6+KPUQCouMFdhUIRnV5l+EWzPHWjuIh7wLChTyMgWbDdD93l8aMnnGYfh1QCNy7A5mEnPLhbbYO0CkUtUNNx/CbmTFaR1NOd7g90AT1avSPkPpOpA6XiG/7xS3+EzW//3Hp/st7h0zpZ0klF9ZiSV8waB27MmdFhayccowY0onQ3UIWilqhJw7/xIy9h19svwZwMvu6c/0DXmhfRPIE/YWT8Nfdhwf/9O6Z87E4sfOLbQL9hYN1HMUfTMO6sj+LNXSuhj54Brb4fcod2Yeim59BXP4x+7DAOaIPAQGjK7cbafmdC07sxpHMLmvS9WNcwFU1dW9Hez3ncfQC6Dq9Ce7/TMOrwKrQ3noahx94BMYYh+l7sSg3Hvr4TkE03Qst1gcDQM7IZIzb8GmnWjR6qx+4Bk4GxM8Hal0LvMxjUMADpXSuQHTEd9knwQ4aNxoIJX8TQaZdhz6Jf4bjmf0THxsWYJXkul54zD3oui1NnXY4FT/bFjMs/H/br8GXYZ3+Bhc/fi1nNF4Ta7tgnn8OCBU9hzoDjghtXCItOuRVDTj0X8vN3FYriQJUYFdHS0sJaW1vL3Q2FQqGoGohoMWOsRaZt79L4FQqFQqEMv0KhUPQ2lOFXKBSKXoYy/AqFQtHLUIZfoVAoehnK8CsUCkUvQxl+hUKh6GUow69QKBS9jIqcwEVEuwG8G2HToQD2JNydWkCdFz7qvHhR54RPNZyX4xljTTINK9LwR4WIWmVnrvUm1Hnho86LF3VO+NTaeVFSj0KhUPQylOFXKBSKXkatGf77y92BCkWdFz7qvHhR54RPTZ2XmtL4FQqFQhFMrXn8CoVCoQigZgw/EV1KRGuJaAMR3Vru/pQKIhpLRK8S0WoiWklEXzGWDyail4lovfH/OGM5EdG9xnl6m4iay/sJigsRpYjoLSL6vfF+AhEtMs7Lk0RUZyyvN95vMNaPL2e/iwURDSKip4hojfGbmaN+KwARfdW4flYQ0RNE1FDLv5WaMPxElALwMwCXATgNwNVEdFp5e1UysgBuZoydCmA2gC8an/1WAK8wxiYBeMV4D+TP0STj7zoAP/fusqb4CoDVtvc/AHCPcV72A7jWWH4tgP2MsYkA7jHa1SL/CeCPjLFTAExD/tz06t8KEY0GcCOAFsbYFAApAB9HLf9WGGNV/wdgDoAXbe+/CeCb5e5Xmc7FswAuArAWwEhj2UgAa43X9wG42tbealdrfwDGIG/ILgTwewCE/CSctPt3A+BFAHOM12mjHZX7MyR8PgYA2OT+XL39twJgNICtAAYb3/3vAVxSy7+VmvD4UfjiTNqMZb0K45HzDACLAAxnjG0HAOP/MKNZbzpX/wHgGwB04/0QAAcYY1njvf2zW+fFWN9htK8lTgCwG8Ajhvz1IBE1opf/Vhhj2wDcDWALgO3If/eLUcO/lVox/MRZ1qvClYioH4DfAriJMXbQrylnWc2dKyL6AIBdjLHF9sWcpkxiXa2QBtAM4OeMsTMAHEFB1uHRG84JjDGNKwFMADAKQCPyMpebmvmt1IrhbwMw1vZ+DID2MvWl5BBRBnmj/zhj7Glj8U4iGmmsHwlgl7G8t5yrswFcQUSbAfwKebnnPwAMIqK00cb+2a3zYqwfCGBfKTtcAtoAtDHGFhnvn0L+RtDbfyvvA7CJMbabMdYD4GkA/799O1ZpIAjCOP6fxoid1hYSEFvLIBaCXep0gnkOsfIFfAMrSxsJaaO9WoQoKppUNrY+wVjsHIiNFiaLu98Pjlz2ttgb5obc7maHgnOllMJ/C2zGKvwSaWFmkHlMC2FmBpwBT+5++uXSAOjHeZ8099+0H8aOjQ7w0bzml8Tdj9x93d03SPlw5e4HwDXQi27f49LEqxf9/9WvuJ+4+zvwZmZb0bQPPFJ5rpCmeDpmthLPUxOXcnMl9yLDHy7QdIEXYAYc5x7PAu97l/SaOQHGcXRJc44j4DU+16K/kXZAzYB70k6G7Pcx5xjtAcM4bwM3wBS4AFrRvhzfp3G9nXvcc4rFNnAX+XIJrCpXHOAEeAYegHOgVXKu6J+7IiKVKWWqR0REfkmFX0SkMir8IiKVUeEXEamMCr+ISGVU+EVEKqPCLyJSGRV+EZHKfALG75tDHUyW2QAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(x2,t3,label='weekend')\n",
    "plt.plot(x2,t4,label='week')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
